An Enhanced Algorithm of Bald Eagle Search for Mobile Robot Path Planning
Guowei Tang, Xiaodong Zhang, Pengfei Hou, Xinyu Yang · 2024
Aiming at the traditional algorithm of Bald Eagle Search (BES) has low convergence efficiency and is difficult to seek out the optimal solution when facing the path planning problem of mobile robot. An enhanced algorithm of Bald Eagle Search (EBES) for solving the global path planning problem of mobile robot is proposed in this paper. Firstly, the bald eagle population is diversified and convergence speed is enhanced by employing Circle Chaotic Mapping for initialization. Secondly, an improved dynamic control factor is introduced to broaden the search range during early iterations, boosting global search capabilities, while gradually reducing its value in later stages to expedite convergence. Finally, the integration of the Cauchy mutation operator facilitates escape from local optima and enhances the algorithm’s global search capabilities. In order to verify the performance and effectiveness of the EBES, four standard test functions and two grid environments with different complexity are selected to carry out performance comparison experiments with traditional BES algorithm, GWO algorithm and IGWO algorithm, and global path planning simulation comparison experiments are carried out. It can be seen from the results that the EBES has good convergence accuracy and optimization performance on four standard test functions. In addition to the average path length, the path standard deviation of the EBES is also better than the three comparison algorithms. The simulation results show the superiority and robustness of the EBES.